Chapter 7:Quantitative Marketing Research

• What Are some of the different uses of quantitative research in marketing research?
  • New product development, brand and community perception, advertising concept research, customer satisfaction
  • What Is The Focus of Each use.
  • NPD = brand read and comparison, optimal features, purchase intent, pricing, and brand acceptance.
  • BP = how are of and familiar with that company and its competitors is the company’s consumer market, which key attributes is the company known for in the marketplace, what makes the company unique in the marketplace.
  • AC = studies that gauge the effectiveness of advertisements prior to full development and production
  • CS = best to deliver the survey asap after the POS interaction.
  • What Are Typical items and elements for each use case.
  • What Are some points of caution when using each method?
• What is validity?
  • Notion that an instrument is measuring what it is supposed to measure
• What Is reliability?
  • Indicator of instruments consistency in its ability to measure.

Chapter 8:Sampling Techniques

• What Is the purpose of sampling?
  • Collecting data from and studying a subset of a population with the goal of being able to use the results from the subset to describe opinions, behaviors and other phenomena of the population at large
• What is the difference between a sample and a population?
  • Sample = a subset of the population
  • population = the whole total
• What is the difference between a parameter and a statistic?
  • Parameter = result that describes data from everyone in the population
  • Statistic = result that describes data from a sample of the population
• How do we determine sample sizes?
  • How many respondents should be surveyed so that a study is representative of the target market
  • What is the confidence level?
    • How confident researchers can be that if a study were replicated the same results would be found
  • What is the margin of error?
    • Indicates how much a statistic can fluctuate when using it to describe the true population
  • What are the standard confidence level and margin of error in Marketing Research?
  • CL = 95%
  • MOE = +/- 5%
  • What Information Is considered in the sample size estimation? What does the formula consider?
  • What are the differences between simple random, stratified random, and systematic sampling?
  • Simple random = a completely random method of selecting subjects. A random number generator
  • Stratified random = a sampling technique where the sample is collected in mutually exclusive groups proportionate to the population
  • Systematic = a sampling method where researchers choose every “nth” record on a list… example every 5th person who receives a phone call
  • What are the differences between convenience, expert, quota, and snowball sampling?
  • Convenience = a technique where the sample is derived from those the researcher has the easiest access to. Friends, family, etc.
  • Expert = the sample is this technique is derived from available experts in the field regarding a certain topic
  • Quota = gathering samples in predefined groups. Age, gender, for example
  • Snowball = participants themselves recruit other members to participate
• What is probability sampling? What are the basic requirements for implementing probability sampling?
  • Uses random selection to ensure members of a population have equal probabilities of being chosen
• What is non-probability sampling?
  • Using the researchers subjective judgment about how the sample should be structured or upon access to the sample
• What are typical sources of samples and sampling frames?
  • Company lists, list brokers, and research panels

Key Concepts:

Sampling

Census

Parameter

Statistic

Sample Size

Population

Confidence Level

Margin of Error

Probability Sampling

Sampling Frame

Non-Probability Sampling

Screening Questions

Chapter 9:Fielding Studies

• What does fielding a study mean?
  • Refers to primary research studies when they are in their data collection phase
• What is the idea and importance of“representative”samples?
  • Makes sure opinions and reactions gathered are not over or under represented from any of the segments within the target population
• What factors should be considered when choosing a survey distribution method?
• Time, cost, response rate, survey length, sensitivity of study topic
• What Are the different distribution methods we studied in class?
• Online, snail mail, telephone
  • Strengths?Weaknesses?
• What is the invitation? Why is it important?
• What is a survey’s response rate?How is it estimated?
  • What are typical response rates for different types of studies?
• What is non-response error bias?
  • What are the common types of non-response bias?
  • How can we deal with them?
• What is response bias?
  • What are common types of response bias?
  • How Can we deal with them?
• What are the differences between confidential and anonymous surveys?

Key Concepts:

Fielding

Representation

OnlineSurveys

Snail Mail Surveys

Telephone Interview Surveys

Response Rate

Non-Response Bias

Response Bias

Confidentiality

Anonymity

Chapter 10:Descriptive Data Analysis

• What is data analysis?
• What are descriptive statistics?
• What Are measures of central tendency?Why Do we use them for?
• What are percentages and what do we use them for?
• What areTop Box Scores? Why are they useful?
• What Are measures of spread? What are they useful for?
• What are cross tabulations? What are they useful for?

Key Concepts:

Data Analysis

Measures of Central Tendency

Mean

Median

Mode

Measures of Dispersion/Spread

Range

Standard Deviation

Percentages

TopBox Score

Top 2 Box Score

Cross Tabulations

Chapter 11:InferentialData Analysis

• What is hypothesis testing? The process of testing a theory. The purpose is to test differences or relationships between two or more groups within a sample to determine if the results are isolated or strong enough to be projected to the target population at large.
  • How do we write null and alternative hypotheses?
    • Null: H0, assumes that there is no difference between the comparison groups in the target population (no effect)
    • Alternative: Ha, predicts that a difference does exist in the target population
• What factors do we consider when picking an inferential statistical test?
  • Make inferences and predictions about a population based on the sample data.
  • Factors depending upon the parameters of a data:
    • How many groups will be tested
    • If the variables are categorical or continuous
    • If the output are mean scores or percentages
• What is the purpose of each of the statistical tests we learned in class?
  • Z-test: The difference between two categorical percentages on a single characteristic
  • T-test: differences of mean scores between two groups. Dependent variable are continuous and two categorical independent variable.
    • Paired T-test: used to test mean scores (continuous variables) when there are pair of ratings from each respondent.
  • Chi-square: testing differences between categorical percentages variables
  • Analysis of variance (ANOVA): Used to test differences of mean scores between 3 or more groups. Dependent variable is continuous and 3 or more independent categorical groups
• What it the p-value? How do we use it to determine support for the null or the alternative hypotheses?
  • The probability that the null hypothesis is true.
  • A P-Value greater than 0.05 means the Ha is true, so we reject the Null
  • A P-Value less than o.o5 means the H0 is true, So we accept the Null

Key Concepts:

HypothesisTesting

Null Hypothesis

Alternative Hypothesis

p-value

Z-test for proportions

Independent Sample T-test

Paired Sample T-test

Chi-square

Analysis of Variance (ANOVA)

Correlation

Chapter 12:Communicating Results

• What Is The purpose of being able to communicate research results effectively? So that the intended audience can understand the results.
• What is data visualization? To display data to best communicate results.
• How do we pick what type charts to use to visualize data?
  • Mean scores:
    • Bar Chart: Continuous mean scores, trend data (results displayed overtime using the same data)
    • Table: Used for large amounts of data, displayed like a likert scale
  • Percentages:
    • Bar chart: Categorical
      • Stacked bar chart: to visualize the distribution of continuous data where each segment of the bar represents the frequency of a corresponding scale point or established range
    • Pie Chart: The data going into the chart must equal 100%
    • Tables: cross tabulations
  • Trending Data Over Time:
    • Line chart: used to show data that changes over time by plotting a series of points and connecting them with a line
    • Bar chart: More versatile bc can compare independent data points
  • Open-ended comments:
    • Reposted in categories in order to uncover major themes (code list) by using a table or word clouds
  • Importance/Performance
    • Used for satisfaction surveys
    • Importance/performance chart: combine both categories into one chart, performance on Y axis and importance on X
• What are the different parts of a research report? What are typical elements presented in each section?
  • Purpose
    • The reason for the project, how results will be used, the research objective, and research questions
  • Methodology
    • State the data collection method, the dates when the data were collected, and study limitations
  • Executive Summary
    • Overview that included top-level results and recommendations, read like an abstract of the report
  • Detailed Findings
    • Summarize and interpret the data collected in logical order
  • Appendices
    • Final section that is used for references

Key Concepts:

Bar Chart

Line Chart

Pie Chart

Stacked Bar Chart

Summary Tables

Word Clouds